A Comparative Study of Classification Techniques for Intrusion Detection

Intrusion detection is one of the major research problems in network security. It is the process of monitoring and analyzing the events occurring in a computer system in order to detect different security violations. The aim of this paper is to classify activities of a system into two major categories: normal and abnormal activities. In this paper we present the comparison of different classification techniques to detect and classify intrusions into normal and abnormal behaviours using WEKA tool. WEKA is open source software which consists of a collection of machine learning algorithms for Data mining tasks. The algorithms or methods tested are Naive Bayes , j48, OneR, PART and RBF Network Algorithm. The experiments and assessments of the proposed method were performed with NSL-KDD intrusion detection dataset. With a total data of 2747 rows and 42 columns will be used to test and compare performance and accuracy among the classification methods that are used.

Intrusion detection is a major research problem in network security. Intrusion Detection Systems (IDS), analyses information from the network trying to identify suspicious behaviors and detect intentions to attack the system. Intrusion attempts are nonlinear with an unpredictable behavior on the network traffic. The process of selecting the key features that allows discriminate attacks from normal traffic, is a crucial task in information security to obtain an effective IDS. We propose in this paper to use an Evolutionary Algorithm and an evaluation function from a classifier, to automatically select key features from a data set before defining a Decision Tree that can be used to discriminate among the network data type. The purpose of this study, is to propose an intrusion detection technique that selects key features using a specially designed evolutionary algorithm with individual evaluations done using C4.5, a wellknown classifier that discriminate data using decision trees. We report very encouraging results of our approach using NSL-KDD intrusion detection benchmark data sets.

Uses of Artificial Intelligent Techniques to Build Accurate Models for Intrusion Detection System

Intrusion detection is a challenging and critical problem in network security. Extensive research activities have been aimed at network-based intrusion detection systems, but most of them are proved unsatisfactory. This paper presents an effective intrusion detection classification model based on projection pursuit. With maximizing a projection index, Projection Pursuit uses Genetic Algorithm to search for the optimal projection direction, projects network connections records from high-dimensional space into 1-dimensional space, and uses the projection values to analyze the data structure and classify the type of intrusion. This intrusion detection classification model not only cuts down the computing complexity in the process of network connection records, but also opens out non-linear structure not like in latent semantics analysis only discovering linear structure, and the results of classification can also be visualized. The results of the experiments on KDD CUP 1999 data show that this model not only has good performance, but also reduces the number of false alarms effectively.

Genetic-fuzzy rule mining approach and evaluation of feature selection techniques for anomaly intrusion detection

In order to discover the network vulnerability timely and solve the very serious problems of network security, this paper puts forward the attack graph which is based on intrusion detection method. The method uses the generation global network attack graph algorithm to build network initial attack graph, and call attack graph optimization algorithm to remove global attack graph unreasonable path, and achieve the goal of simply attack graph. Finally, management personnel get the basis which is computed nodes in each state attack graph algorithms degree of loss to optimize the network security. Experiments show that the intrusion detection method is reasonable and effective, and has the advantages of simple and easy.

A new round of scientific and technological revolution and industrial reform promote the intelligent development of automobile and promote the deep integration of automobile with Internet, big data, communication and other industries. At the same time, it also brings network and data security problems to automobile, which is very easy to cause national security and social security risks. Intelligent vehicle Ethernet intrusion detection can effectively alleviate the security risk of vehicle network, but the complex attack means and vehicle compatibility have not been effectively solved. This research takes the vehicle Ethernet as the research object, constructs the machine learning samples for neural network, applies the self coding network technology combined with the original characteristics to the network intrusion detection algorithm, and studies a self-learning vehicle Ethernet intrusion detection algorithm. Through the application and test of vehicle terminal, the algorithm generated in this study can be used for vehicle terminal with Ethernet communication function, and can effectively resist 34 kinds of network attacks in four categories. This method effectively improves the network security defense capability of vehicle Ethernet, provides technical support for the network security of intelligent vehicles, and can be widely used in mass-produced intelligent vehicles with Ethernet.

One of the problems in computer network security is Intrusion Detection. To detect it, we need a NIDS (Network Intrusion Detection). One example of NIDS is a honeypot. In this research, we use a kippo honeypot as a tool as an IDS. In this paper, honeypot logs are used for classifying SSH attacks using machine learning. The research used the original dataset from Kippo's log to classify the ssh attack as the primary source. We implement four algorithm classifications: Decision Tree, Naive Bayes, SVM, and Random Forest. Our research found that Decision Tree (DT) and random forest have the same score and better accuracy than naive Bayes and SVM. The accuracy and F1 score of RF and DT are 0.92 and 0.92, respectively.

The modern world technology is civilized, globalized and modernized. The technological development of social networks and e-commerce applications produce larger data. This data communication is major task, because device to device communication need network terminal. This data transmission is not safe because of different types of tools and software available to destroy the existing network. In the field of network security during data transfer from one particular node to other node some security vulnerability is happened this is the one of the critical issue in this sector. The reason for this network security is different types of data attacks are happen in day to day life. It is easy to establish a new network but protecting the entire network is a big issue. This network security is generally two parameter first one is communication and second one is data automation. The network security field is directly or indirectly linked with the concept of data encryption. The development in this network security has taken us to a level that from signature again we came back to thumb print. For example maintain the data secure we use the lock system which is a finger print type. This technology helps us to protect the physical data theft, but logical data theft is still problem for data transmission. This article will brief about the network security it also presents the various network security types. Those types are wired and wireless network security. Apart from the network security the following topics is also discussed in this article. Those are network security protocols and simulation tools in network security. The research problems in network security are privacy and vulnerability of data.

Achieving cyber-security has grown more challenging in recent years. There is a need for a strong defense system against various cyberattacks to protect the data from the threat and damage. We have developed the hybrid model based on crow search optimization (CSO) and random forest (RF) algorithm for identifying threats and irregularities in a computer network. In comparison to the various state-of-the-art classifiers, the CSO method was applied to the NSL-KDD dataset and the ROSPaCe dataset for the detection of the attacks. In the proposed work, we have used a random forest technique to perform feature selection (FS) to improve the effectiveness and efficiency of intrusion detection. We have also created a confusion matrix over different kinds of attacks concerning predicted class and attack class, enabling us to evaluate the performance of a classification model for various attack groups without taking CSO and after applying the CSO approach. In this study, the various performance matrix parameters have been evaluated in terms of all classifiers along with the proposed model. In the NSL-KDD dataset, the proposed model offers 88.48% accuracy for binary distribution and 86.92% accuracy for multiclass distribution, and the detection rate of DoS attack is 99.9%, Probe is 97.8%, R2L is 99.7%, and U2R is 97.1% respectively. In the ROSPaCe dataset, the accuracy is 98.6%, and in the CICIoT2023 dataset, the accuracy is 99.92%. As compared to the previous models like NB Tree, Naïve Bayes, SVM, and other previous models, the implemented RF-CSO offers better detection accuracy and a low false alarm rate compared in the result section.

The cyber assault has emerged as a serious digital threat. Day after day, hackers inflict severe financial harm on nations and people alike. As a result, threat detection has become increasingly critical for computer system security going forward. This research focused on seeing if it can recognize and anticipate dangers based on complicated instances by using machine learning. This can help identify and prevent attacks before they happen. Many well-known threat classification methods are tested in this work. It is centered around six of the most prominent machine learning (ML) techniques used in intrusion detection research: Random Forest, Naive Bayes, Artificial Neural Networks, K-nearest neighbor algorithm, Support Vector Machines, and bagging. In this study, a machine learning-based strategy for reliably predicting dos, R2L, U2R, probe, and overall attacks was established by assessing the accuracy of several ML tactics. Finding effective machine learning algorithms, as well as determining critical qualities that can provide the best outcomes, is a massive task for academics. This paper also examines the benefits and drawbacks of each method before applying it to the NSL-KDD dataset to evaluate how well they perform. The novelty in this research work lies in the fact that it utilizes various feature selection methods to find the set of features that have the greatest impact on classification performance. Analysis of the results using different performance metrics revealed improved accuracy. For the NSL-KDD dataset’s intrusion detection and attack type classification tasks, the highest detection rate was obtained with 99.86%.

With the rapid development of Internet technology, computer has become an indispensable part of modern society. However, with the popularization of computer technology, the problem of network security is becoming increasingly prominent. In recent years, China's network security incidents occur frequently. As a means of attack, computer deception has seriously threatened national information security, social stability and people's life. Therefore, it is of great practical significance to study the role of computer deception in network security. Its purpose is to mislead and deceive users or systems by various means to achieve the purpose of illegally obtaining information, damaging the stability of the system or carrying out other illegal activities. Based on this, this paper aims to explore the role of computer deception in network security. Firstly, the concept, content and present situation of computer network security are expounded, and then the necessity of computer network security management and protection is analyzed. Secondly, the related technology of computer deception is introduced. Finally, the application of computer spoofing in network security is studied, and the effectiveness of computer spoofing in preventing and detecting network attacks is revealed. In addition, the ethical issues of computer deception and its potential in future cybersecurity strategies are discussed. Research shows that computer deception can not only enhance the defense mechanism, but also improve the organization's cognition and response ability to network threats, and become an indispensable security strategy. By evaluating current technologies and methods, this paper provides practical recommendations for researchers and practitioners to effectively use computer spoofing techniques to improve network security.

The dynamic nature of communication paradigms demands uninterrupted connectivity in order to achieve maximum functionality. It will take creative thinking to achieve this smoothness. It becomes clear that nonlinear analysis is a revolutionary methodology that provides original insights and solutions. In order to attain seamless connectedness, this study investigates how nonlinear analysis might be applied to revolutionise communication paradigms. A strong foundation for comprehending and improving communication systems is provided by nonlinear analysis, which has its roots in the study of complex systems. This paper delves into the fundamental concepts of nonlinear analysis and elucidates how they may be applied to the complicated dynamics of modern communication. Nonlinear analysis, which includes information theory, signal processing, and chaos theory, introduces a new paradigm in communication technology. Network congestion, signal distortion, and security holes are just a few of the issues plaguing today's communication infrastructure that cause disruptions in service. Nonlinear analysis is a useful technique for overcoming and reducing these challenges. The efficiency, dependability, and security of communication networks can all be improved by implementing nonlinear system dynamics. In this study, nonlinear analysis is applied to real-world problems in network security, wireless communication, and the Internet of Things (IoT) through several detailed case studies. Using these real-world examples, we can see how nonlinear methods can successfully alter established modes of communication and demonstrate their value. The implications of nonlinear analysis for future communication paradigms are far-reaching and extend beyond the capabilities of current technology. This essay delves into the future of communication technology, shedding light on emerging trends that may have profound impacts in the years ahead. It also examines the benefits and difficulties that come with employing nonlinear analysis to establish seamless communication. Nonlinear analysis is a potent instrument with potential applications in shifting communication paradigms, facilitating seamless connectivity, and paving the way for the creation of novel forms of communication technology. The study emphasises the significance of nonlinear techniques in addressing the challenges of modern communication systems and guiding them towards a more connected and successful future.

Distributed Denial of Service (DDoS) attack is one of the most long-lasting problems in network security. Recently, although the DDoS attack mechanisms are widely understood, the problems are becoming more frequent due to the similarity between DDoS attack and normal traffic. In this work, we propose two approaches of DDoS attack detection which are based on the Self-Organizing Map (SOM). The proposed algorithms with their detection architecture are implemented in the Software-Defined Networking (SDN) technology which has the flexibility and programmable abilities. The SDN controller allows us to quickly perform complex classification and detection algorithm. By deploying a testbed environment, we successfully evaluate our proposed algorithms in terms of both accuracy and computational overhead. The experimental results show that these algorithms can reduce the processing time while maintain the suitable accuracy rate.

In order to solve wireless local area network (WLAN) security problem due to the open-wide nature of wireless radio and the improvement of computing power, a design of WLAN security program based on near field communication (NFC) is presented in this paper. In this paper, the importance of having access to handshake for WPA2 brute force is explained. The proposed design protects the four-way handshake by taking advantage of NFC short-range character to eliminate the risk of intercept. For implementation, Android system is selected as a mobile device development platform. The design is compatible with the IEEE 802.11i which ensures the massive expansion in the future. Furthermore, the design simplifies operations to improve users’ experience without much extra hardware cost and offers an option to the owner of WLAN to control the access physically, which benefits commercialization of NFC. From one perspective, this design can solve the wireless network security problem effectively.KeywordsWireless Local Area NetworkNear Field CommunicationWireless RouterDictionary AttackAndroid SystemThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.